What's included:
The recent emergence of chatbots and AI-driven research support tools has the potential to challenge, support, and significantly alter our approaches to science and scientific knowledge production. AI tools and chatbots are becoming ingrained in almost all aspects of scientific knowledge production, from supporting topic discovery, scholarly writing, and assessment, to understanding theoretical elaboration, disciplinary and contextual relevance, and copyright issues. AI-powered tools can either support or distort our ability to craft arguments in writing; they may affect our ability to defend specific claims in research; they can assist with topic discovery and information overload; they can strengthen or dilute academic writing; AI can help with developing legitimisation criteria for research, but it can also significantly challenge what scientists consider to be the core of scientific research. The reality is that researchers, supervisors, and postgraduate students should remain vigilant and ethical, and continually seek ways to protect the relevance of scientific endeavours.
In this three-part series, along with two additional workshops, participants will:
- Learn how AI can inform, strengthen, or distort scientific research.
- Explore the limitations of AI in scholarly research.
- Discover useful support and detection tools.
- Explore AI ethics, authorship, and copyright issues.
- Reflect on scientific relevance in the wake of AI tools and chatbots.
Note:
- The duration of each workshop may be subject to adjustments based on participant engagement and the depth of discussions.
- Each workshop can be split into two sessions of approximately 90 minutes each.
Artificial Intelligence and scholarly writing
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This 2 ½ hour workshop explores the basic principles of scientific writing and how AI-powered research tools and chatbots can be useful.
In this workshop, participants will:
- Gain an understanding of the basic principles of scientific research, distinguishing its characteristics from other forms of inquiry.
- Acquire knowledge about the basic principles of scientific argumentation and their application in research.
- Familiarise themselves with the basic claims of scientific research.
- Examine the ways in which AI can contribute to different phases of academic writing and its utility in the process.
- Explore legitimisation criteria for evaluating research and the potential role of AI.
- Explore constructive alignment in research and where AI can be useful.
- Evaluate the application of AI in enhancing coherence and consistency in academic writing, particularly in relation to “weaving the golden thread” in research.
- Assess the value of AI in writing genres and its potential influence on individual writing styles.
Explore AI prompting strategies for different aspects of scientific knowledge production.
Artificial Intelligence and topic discovery
This 2 ½ hour workshop introduces approaches to research topic discovery and explores useful AI-driven search and discovery tools to support the process.
In this workshop, participants will:
- Investigate how AI can assist in dealing with information overload.
- Explore the impact of AI and emerging approaches on conducting literature reviews and topic discovery.
- Examine strategies for organizing literature using AI tools.
- Explore ethical AI-authoring and useful detection tools.
- Apply techniques for crafting and assessing introductions to research.
Explore assessment criteria for research topics and research questions and examine the potential benefits of AI tools.
Artificial Intelligence and the core of scientific research
This 2 ½ hour workshop will explore the core of scientific research and discuss strategies for maintaining relevance and rigour in the context of AI-driven writing and support tools.
In this workshop, participants will:
- Investigate theory, philosophy, and argumentation in scientific research, examining whether AI strengthens or dilutes these core concepts of science.
- Evaluate different styles of reasoning and the potential for AI to create distorted conceptual lenses.
- Explore the need for exactness and rigour in science and the possibility of utilising incoherent evidence that supports claims generated by AI.
- Investigate the role of AI in theoretical elaboration and making data-theory links.
- Analyse the impact of AI on qualitative data analysis, interpretation, and articulation of research findings, considering whether AI can impede the ability to analyse data and draw inferences.
- Evaluate the limitations of AI in ensuring constructive alignment and consider whether AI can assist in crafting a comprehensive "big picture" in research.
- Explore the use of AI in generating ideas, considering whether AI can hinder thinking and reflection.
- Explore ethical considerations surrounding AI and its implications for authorship in scientific research.
- Re-evaluate teaching and assessment for a basic research methodology module in response to the emergence of AI-driven tools and resources.
Examine the consequences of questionable science and the influence of a publish-or-perish publication culture.
Optimising Statistical Research Using AI (Optional)
This 2-hour workshop will explore the use of AI in conducting statistical analysis in scientific research, discussing possible applications of AI with statistical packages, as well as potential pitfalls and the importance of following proper statistical analysis criteria.
In this workshop, participants will:
- Explore essential criteria for conducting sound statistical analysis, ensuring the generation of reliable and meaningful results that contribute to the advancement of knowledge.
- Explore how researchers can leverage AI to enhance their research efficiency and streamline statistical analysis using popular programs like R and SPSS.
- Gain insights into practical examples where AI guides statistical analysis through the creation of R programs or the utilization of menu-driven functionalities in SPSS.
- Explore the potential of AI as a valuable tool for assisting researchers in interpreting statistical analysis.
- Critically evaluate and validate the interpretations generated by AI, leveraging domain knowledge and understanding of the research context.
- Explore the limitations of AI when employed in statistical analysis with tools such as R and SPSS.
- Investigate scenarios where AI may encounter challenges or provide imperfect solutions, enabling researchers to make informed decisions and exercise caution.
- Explore ethical considerations in quantitative research and the integration of AI.
Gain insights into ethical AI best practices, potential risks, and strategies to mitigate them, ensuring the responsible use of AI in statistical analysis.